scholarly journals Optimization method of emergency logistics network in the initial post-earthquake stage considering multiple factors and the case analysis

2021 ◽  
Vol 40 (1) ◽  
pp. 1357-1366
Author(s):  
Junxiang Xu ◽  
Jingni Guo ◽  
Jin Zhang ◽  
Yongdong Sun ◽  
Weihua Liu ◽  
...  
Keyword(s):  
Hybrid Genetic Algorithm ◽  
Optimization Method ◽  
Allocation Model ◽  
Logistics Network ◽  
Location Allocation ◽  
Emergency Logistics ◽  
Initial Stage ◽  
Optimal Target ◽  
The Difference ◽  
Location Allocation Problem

Based on the traditional reliable location problem and the facility location-allocation problem (FLAP), this paper firstly considered the factors of staged characteristics, facility disruption scenarios, multiple types of fuzzy demand, and facility capacity limitations in the initial stage of post-earthquake rescue. Then, a location-allocation model of emergency facilities suitable to be applied in the initial stage of post-earthquake rescue was established. On this basis, considering the characteristics of the model, a hybrid genetic algorithm with integer coding was designed. Finally, taking the Wenchuan Earthquake as a case, numerical simulation and analysis were conducted, which verified the effectiveness of the model and algorithm proposed in this research. The results of this paper can optimize the system target by more than 21.6%; even if there is no emergency facility disruption after the earthquake, the difference between the optimization result and the optimal target value is only 8.3%.

scholarly journals A Novel Hybrid Algorithm for Multiobjective Location-Allocation Problem in Emergency Logistics

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Hongrui Chu ◽  
Yahong Chen
Keyword(s):  
Optimization Model ◽  
Complex Model ◽  
Theory And Practice ◽  
Logistics System ◽  
Logistics Network ◽  
Location Allocation ◽  
Emergency Logistics ◽  
Ga Algorithm ◽  
Relief Distribution ◽  
Deprivation Costs

Increased frequency of disasters keeps reminding us of the importance of effective resource distribution in postdisaster. To reduce the suffering of victims, this paper focuses on how to establish an effective emergency logistics system. We first propose a multiobjective optimization model in which the location and allocation decisions are made for a three-level logistics network. Three objectives, deprivation costs, unsatisfied demand costs, and logistics cost, are adopted in the proposed optimization model. Several cardinality and flow balance constraints are considered simultaneously. Then, we design a novel effective IFA-GA algorithm by combining the firefly algorithm and genetic algorithm to solve this complex model effectively. Furthermore, three schemes are proposed to improve the effectiveness of the IFA-GA algorithm. Finally, the numerical results provide several insights on the theory and practice of relief distribution, which also illustrate the validity of the proposed solution algorithm.

scholarly journals An Algorithm to Manage Transportation Logistics That Considers Sabotage Risk

2018 ◽  
Vol 8 (3) ◽  
pp. 39 ◽  
Author(s):  
Chaiya Chomchalao ◽  
Sasitorn Kaewman ◽  
Rapeepan Pitakaso ◽  
Kanchana Sethanan
Keyword(s):  
Differential Evolution ◽  
Computational Result ◽  
Optimal Solution ◽  
Computational Time ◽  
Location Allocation ◽  
De Algorithm ◽  
Large Numbers ◽  
The Difference ◽  
Location Allocation Problem ◽  
The Mathematical Model

This paper presents an algorithm to solve the multilevel location–allocation problem when sabotage risk is considered (MLLAP-SB). Sabotage risk is the risk that a deliberate act of sabotage will happen in a living area or during the transportation of a vehicle. This can change the way decisions are made about the transportation problem when it is considered. The mathematical model of the MLLAP-SB is first presented and solved to optimality by using Lingo v. 11 optimization software, but it can solve only small numbers of test instances. Second, two heuristics are presented to solve large numbers of test instances that Lingo cannot solve to optimality within a reasonable time. The original differential evolution (DE) algorithm and the extended version of DE—the modified differential evolution (MDE) algorithm—are presented to solve the MLLAP-SB. From the computational result, when solving small numbers of test instances in which Lingo is able to find the optimality, DE and MDE are able to find a 100% optimal solution while requiring much lower computational time. Lingo uses an average 96,156.67 s to solve the problem, while DE and MDE use only 104 and 90 s, respectively. Solving large numbers of test instances where Lingo cannot solve the problem, MDE outperformed DE, as it found a 100% better solution than DE. MDE has an average 0.404% lower cost than DE when using a computational time of 90 min. The difference in cost between MDE and DE changes from 0.08% when using 10 min to 0.54% when using 100 min computational time. The computational result also explicitly shows that when sabotage risk is integrated into the method of solving the problem, it can reduce the average total cost from 32,772,361 baht to 30,652,360 baht, corresponding to a 9.61% reduction.

scholarly journals Solving a Two-Stage Stochastic Capacitated Location-Allocation Problem with an Improved PSO in Emergency Logistics

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Ye Deng ◽  
Wanhong Zhu ◽  
Jian Tang ◽  
Jianfei Qin
Keyword(s):  
Two Stage ◽  
Location Allocation ◽  
Adaptive Parameter ◽  
Emergency Logistics ◽  
Decision Variables ◽  
Expected Value Model ◽  
Restart Strategy ◽  
Value Model ◽  
Location Allocation Problem ◽  
Capacitated Location

A stochastic expected value model and its deterministic conversion are developed to formulate a two-stage stochastic capacitated location-allocation (LA) problem in emergency logistics; that is, the number and capacities of supply centers are both decision variables. To solve these models, an improved particle swarm optimization algorithm with the Gaussian cloud operator, the Restart strategy, and the adaptive parameter strategy is developed. The algorithm is integrated with the interior point method to solve the second-stage model. The numerical example proves the effectiveness and efficiency of the conversion method for the stochastic model and the proposed strategies that improve the algorithm.

scholarly journals Typhoon track change–based emergency shelter location–allocation model: a case study of Wenchang in Hainan province, China

2019 ◽  
Vol 26 (3) ◽  
pp. 196-203 ◽  
Author(s):  
Lianjie Qin ◽  
Wei Xu ◽  
Xiujuan Zhao ◽  
Yunjia Ma
Keyword(s):  
Local Governments ◽  
Allocation Problem ◽  
Hainan Province ◽  
Allocation Model ◽  
Location Allocation ◽  
Emergency Shelters ◽  
Typhoon Track ◽  
Proposed Model ◽  
Particle Swarm Optimisation Algorithm ◽  
Location Allocation Problem

BackgroundDetermining the locations of disaster emergency shelters and the allocation of impacted residents are key components in shelter planning and emergency management. Various models have been developed to solve this location–allocation problem, but gaps remain regarding the processes of hazards. This study attempts to develop a model based on the change of typhoon track that addresses the location–allocation problem for typhoon emergency shelters.PurposeTo consider the changes in candidate shelters and number of evacuees due to the change in impact area with the progression of a typhoon.MethodsThe proposed model is composed of several static processes and solved by a modified particle swarm optimisation algorithm with a restart strategy.ResultsThe model is illustrated with the case of the evacuation process for Wenchang in Hainan province during Typhoon Rammasun in 2014 and Typhoon Mirinae in 2016. For the case of Typhoon Rammasun in 2014, the residents from east to west need to evacuate in three phases. For the case of Typhoon Mirinae in 2016, residents in the northern communities need not to evacuate to candidate shelters because they are not affected by the typhoon.ConclusionThe proposed model has advantages compared with non-typhoon track change–based model in saving time spent in shelters for residents and saving public resources for the local governments. With the proposed model, a manager could efficiently evacuate residents by considering the typhoon conditions.

Reverse Logistics Network Design Using a Hybrid Genetic Algorithm and Simulated Annealing Methodology

2011 ◽  
pp. 168-186 ◽  
Author(s):  
Gülfem Tuzkaya ◽  
Bahadir Gülsün ◽  
Ender Bildik
Keyword(s):  
Genetic Algorithms ◽  
Simulated Annealing ◽  
Network Design ◽  
Reverse Logistics ◽  
Hybrid Genetic Algorithm ◽  
Logistics Network ◽  
Wilcoxon Rank Sum Test ◽  
Reverse Logistics Network ◽  
Network Coordination ◽  
The Difference

Reverse logistics network design (RLND) effectiveness has an important impact on the effectiveness of the whole supply network coordination. Considering that, in this study, the RLND problem is investigated and a hybrid genetic algorithms and simulated annealing (HGASA) methodology is proposed. This problem is applied to a preceding study which utilized genetic algorithms (GA) for the optimization. HGASA and GA results are tested with Wilcoxon rank-sum test for hundred runs and the results prove the difference between two approaches. Additionally, the averages and the standard deviations support that, the HGASA algorithm increases the probability of obtaining better solutions.

Sign in / Sign up

Export Citation Format

Share Document